We propose that assignment inference for visualizations of continuous data can be simulated using a weighted sum over multiple sources of merit. With knowledge on how much weight to put on merit from direct associations (\(W_A\) ) and the dark-is-more bias (\(W_D\) ), we can combine these sources of merit (combined merit bipartite graph in Fig. REF ) and use established methods for simulating assignment inference [1]}, [2]}, [3]} to predict inferred mappings. In the top row, these sources of merit are consistent, and simulating assignment inference over combined merit predicts observers will infer that darker colors map to larger quantities. In the bottom row, these two sources of merit are conflicting. Depending on the relative weight given to each source, they might cancel each other out, or one might dominate over the other. The weights used in Fig. REF  are based on the results of Exp. 3, with greater weight on direct associations than on the dark-is-more bias (see Exp. 3 for details).
